About the Role :

CloudAI Technologies is seeking an experienced Cloud Data Architect to lead the architecture and design of modern enterprise data platforms across AWS and Azure, with strong expertise in Databricks, Snowflake, data lakes, and lakehouse architectures.

This is a senior technical leadership role responsible for defining data architecture, technology standards, reference architectures, and implementation patterns for complex cloud data modernization initiatives.

Key Responsibilities :

- Define end-to-end architecture for enterprise data lakes, lakehouses, cloud data warehouses, and modern data platforms.

- Architect solutions using Databricks, Snowflake, AWS, and Azure.

- Develop reference architectures, technical standards, design patterns, and engineering guidelines for CloudAI's data practice.

- Design scalable architectures for data ingestion, transformation, storage, processing, governance, analytics, AI/ML, and data consumption.

- Define appropriate technology choices based on workload, performance, scalability, security, cost, and business requirements.

- Architect medallion and layered lakehouse architectures.

- Establish standards for data quality, metadata management, lineage, cataloging, governance, security, privacy, and access control.

- Establish engineering patterns for Python, SQL, Spark/PySpark, Delta Lake, Databricks, and Snowflake.

- Lead performance and cost optimization strategies across cloud compute, storage, Databricks, Snowflake, and data-processing workloads.

- Provide technical leadership and mentorship to data engineers and senior engineers.

- Collaborate closely with CloudAI's AI practice to design data platforms that support Generative AI, RAG, enterprise search, semantic retrieval, machine learning, and agentic applications.

Required Qualifications :

- 10+ years of professional experience in data engineering, data architecture, data platforms, or related disciplines.

- Demonstrated experience architecting and delivering enterprise-scale cloud data platforms.

- Strong expertise with Databricks and/or Snowflake.

- Strong architecture and implementation experience with AWS and/or Microsoft Azure.

- Advanced understanding of SQL, Python, Spark/PySpark, and data engineering principles.

- Strong understanding of cloud storage technologies such as Amazon S3 and Azure Data Lake Storage.

- Experience leading architecture and design discussions with engineering teams and customer stakeholders.